apache-hudi-expert

Apache Hudi table types differ by write and read performance trade-offs. Copy-on-Write stores columnar data exclusively for faster reads, while Merge-on-Read combines log files and columnar data to enable faster streaming writes at the cost of read-time merging.

Hudi Streamer enables continuous data ingestion for CDC pipelines by extracting changes from source systems and writing them to Hudi tables. You configure it to manage incremental data loads while maintaining exact change data capture state.

Compaction merges log files into base columnar files to optimize read performance for Merge-on-Read tables, while clustering reorganizes data layout to improve query pruning. Use them to maintain production lakehouse performance.

Yes, Apache Hudi supports snapshot, incremental, and time travel queries directly on object storage. These query capabilities allow you to read historical table states and process only newly changed data efficiently.

Apache Hudi provides Optimistic Concurrency Control (OCC), Non-blocking Concurrency Control (NBCC), and Multi-Version Concurrency Control (MVCC) to manage simultaneous streaming ETL writes and prevent data conflicts.

Yes, understanding Spark, object storage, and core data lake concepts is required. Apache Hudi relies heavily on Spark for executing write and read operations, configuring indexing strategies, and running table services.